KDD 2019 Research Track Oral Presentations: RT11 Clustering and Visualization

[まとめページへのリンク](https://qiita.com/kotaaaa/items/4f144d660d504cc091cf)

#Research Track Oral Presentations: RT11 Clustering and Visualization

## Deep Landscape Forecasting for Real-time Bidding Advertising 
Kan Ren (Shanghai Jiao Tong University); Jiarui Qin (Shanghai Jiao Tong University); Lei Zheng (Shanghai Jiao Tong University); Zhengyu Yang (Shanghai Jiao Tong University); Weinan Zhang (Shanghai Jiao Tong University); Yong Yu (Shanghai Jiao Tong University)


##### Bid landscape forecasting 
Simple statistic -based (KDD16, ECML16)
Function form assumption 

##### Survival analysis methodology 
Survival analysis: time-to-event data analysis 

##### Related work
Deep learning with survival analysis 
Deep landscape forecasting 
Deep learning solution with solution 
Inference 
Losing probability by multiply conditional prob

Model architecture.
Feature vector x and varying price feature vector 
Loss function: 

- uncensored data 
- Based on C.D.E

Visualized analysis

##### Conclusion 

- A novel deep learning method for market price modeling 
- And distribution learning , in real-time bidding advertising 
- Fine-grained forecasting for individual auction

QA

features

- User from web brousa 
- Adviser itself

Applied 2nd price auction 

Background 

- Real time bidding 


##### Predicting Path Failure In Time-Evolving Graphs
Jia Li (The Chinese University of Hong Kong); Zhichao Han (The Chinese University of Hong Kong); Hong Cheng (The Chinese University of Hong Kong); Jiao Su (The Chinese University of Hong Kong); Pengyun Wang (Noah’s Ark Lab, HuawTechnologies); Jianfeng Zhang (Noah’s Ark Lab, Huawei Technologies); Lujia Pan (Noah’s Ark Lab, Huawei Technologies)



##### Focus: Path classification in  a time 0rvoluving graph, which predicts the status od a path in the near future
Define: 

- Time-evolving graph 
- Path availability 
- Path classification 

Three properties

- Node correlation 
- Graph structure dynamics
- Temporal dependency 

Model: two-layer LRGCN, obtain the hidden representation and Self-attentive 
Static graph modeling 
Relational GCN (R-GCN) by Kipf er al 

Two-hop simplified 
Adjacent graph snapshots modeling 
Before diving into sequence of graph snapshot, we first focus on two adjacent time step  t-1 and t 
Time- evolving graph unit 

##### Two challenges 

- Size invariance
- Node importance 
SAPE: self-attentive path embedding method
- (Data: predicting path failure)

The benefit of graph evolution modeling 
Training efficiency 

conclusion
Path classification in time-evolution 


## Pairwise Comparisons with Flexible Time-Dynamics 
Lucas Maystre Spotify lucasm@spotify.com Victor Kristof EPFL victor.kristof@epfl.ch Matthias Grossglauser EPFL matthias.grossglauser@epfl.ch

##### background
How can we quantify the skill of France team?
relate work: Latent skill model 
Actual setting: Data come with a timestamp

We might want to ask 
How strong was France in 1972? In 2018?

This work: kickscore: skill becomes a (latent) stochastic process
Covariance function defines time dynamics
Model is conditionally parametric
Covariance function 
(Model inference: )
Inference algorithm
For each observation: using EP [minka, 2001] or CVI[khan et al ., 2017] 
For each item: using SSM reformulation 

##### Conclusion: 

- Pairwise comparison model with Flexi bee time-dynamics
- Linear-time inference algorithm


## Modeling Extreme Events in Time Series Prediction 

Daizong Ding, Mi Zhang∗ Fudan University, China Xudong Pan, Min Yang Fudan University, China  Xiangnan He University of Science and Technology of China

##### background: 

- time series prediction, RNN
##### relate-work: 

- extreme events in time-series data
- 
Estimated distribution of labels y
Extreme event problem in DNN
Recalling extreme event in history 
Attention mechanism
Extreme value theory
Extreme value loss
Conclusion: 
Optimization:
Extreme events in time-series data 
Why DNN is innately weak in predicting extreme events
(A framework )


## Adversarial Substructured Representation Learning for Mobile User Profiling 
Pengyang Wang∗ Missouri Univ. of Sci. and Tech. MO, USA pwqt3@mst.edu Yanjie Fu∗† Missouri Univ. of Sci. and Tech. MO, USA fuyan@mst.edu Hui Xiong Rutgers University NJ, USA hxiong@rutgers.edu Xiaolin Li Nanjing University Nanjing, China lixl@nju.edu.cn


##### background:
Mobile user profiling, similarity graph of users, transportation, OD pairs
Adaptive interfaces by inferring trip 
Human activities are spatially , temporally, and socially structural 
Spatial-temporal transition patterns
(Given a  user and corresponding user activity)
Preserving entire-structure 
Preserving substructures
Approximating substructure detector
(Summary: generator auto encoder linked with an approximated )
Study of Node and circle substructures

##### Research problem 

- Method users as activity graphs 
- Formulate modeling specific interests as preserving substructures of user activity graphs
- Propose an adversarial substructure learning model to integrate substructure into representations

relate-work:
Conclusion: